Effects of polymorphisms in beta1-adrenoceptor and alpha-subunit of G protein on heart rate and blood pressure during exercise test. The finnish cardiovascular study.

J Appl Physiol vol 100, no 2, pp 507-511, 2006

Monte Carlo Methods in Parameter Estimation of Nonlinear Models

Yksi keskeisimmistä tehtävistä matemaattisten mallien tilastollisessa analyysissä on mallien tuntemattomien parametrien estimointi. Tässä diplomityössä ollaan kiinnostuneita tuntemattomien parametrien jakaumista ja niiden muodostamiseen sopivista numeerisista menetelmistä, etenkin tapauksissa, joissa malli on epälineaarinen parametrien suhteen. Erilaisten numeeristen menetelmien osalta pääpaino on Markovin ketju Monte Carlo -menetelmissä (MCMC). Nämä laskentaintensiiviset menetelmät ovat viime aikoina kasvattaneet suosiotaan lähinnä kasvaneen laskentatehon vuoksi. Sekä Markovin ketjujen että Monte Carlo -simuloinnin teoriaa on esitelty työssä siinä määrin, että menetelmien toimivuus saadaan perusteltua. Viime aikoina kehitetyistä menetelmistä tarkastellaan etenkin adaptiivisia MCMC menetelmiä. Työn lähestymistapa on käytännönläheinen ja erilaisia MCMC -menetelmien toteutukseen liittyviä asioita korostetaan. Työn empiirisessä osuudessa tarkastellaan viiden esimerkkimallin tuntemattomien parametrien jakaumaa käyttäen hyväksi teoriaosassa esitettyjä menetelmiä. Mallit kuvaavat kemiallisia reaktioita ja kuvataan tavallisina differentiaaliyhtälöryhminä. Mallit on kerätty kemisteiltä Lappeenrannan teknillisestä yliopistosta ja Åbo Akademista, Turusta.

Computing the persistent homology of range images with alpha shapes

Laser scanning is becoming an increasingly popular method for measuring 3D objects in industrial design. Laser scanners produce a cloud of 3D points. For CAD software to be able to use such data, however, this point cloud needs to be turned into a vector format. A popular way to do this is to triangulate the assumed surface of the point cloud using alpha shapes. Alpha shapes start from the convex hull of the point cloud and gradually refine it towards the true surface of the object. Often it is nontrivial to decide when to stop this refinement. One criterion for this is to do so when the homology of the object stops changing. This is known as the persistent homology of the object. The goal of this thesis is to develop a way to compute the homology of a given point cloud when processed with alpha shapes, and to infer from it when the persistent homology has been achieved. Practically, the computation of such a characteristic of the target might be applied to power line tower span analysis.

Prognostic Markers and Prolonged Interferon-alpha Therapy in Renal Cell Carcinoma

Molekyylimarkkerit ja pitkäaikainen alfainterferonihoito munuaissyövässä Munuaissyöpäpotilaiden viiden vuoden elossaololuku on noin 50 %. Aikaisempien tutkimuksien mukaan viiden vuoden elossaololuku metastasoituneessa munuaissyövässä on 3-16 %, kun käytettiin alfainterferonia sisältävää hoitoa. Tyypillisesti alfainterferonia on käytetty vähemmäin kuin 6 kuukautta. Avoimia kysymyksiä ovat alfainterferonin optimaalinen hoitoannos ja hoidon kesto yksin tai yhdessä uusien täsmähoitojen kanssa. Tärkeimmät tavoitteet olivat tutkia 1) jaksotetun pitkäaikaisen alfainterferonihoidon tehoa ja siedettävyyttä metastasoituneessa munuaissyövässä ja 2) p53-, Ki-67- ja COX-2-proteiinituotannon ennusteellista merkitystä munuaissyövässä. Tutkimuksessa 117 metastasoituneelle munuaissyöpää sairastaneelle potilaalle etsittiin yksilöllinen hänen sietämänsä maksimaalinen hoitoannos rekombinanttia alfa2a-interferonia (Roferon-ATM). Hoitoa pyrittiin jatkamaan 24 kuukauden ajan. Kolmen hoitoviikon jälkeen pidettiin yhden viikon tauko. Hoito lopetettiin, jos ilmaantui vakavia haittavaikutuksia tai tauti eteni. Toisessa tutkimuksessa proteiinituotanto analysoitiin immunohistokemiallisesti munuaissyöpäpotilaiden kasvainnäytteistä, joita oli säilytetty parafiinissa. Kasvainnäytteet oli otettu talteen munuaisen poistoleikkauksen yhteydessä. Nämä potilaat jaettiin kolmeen eri ryhmään: metastasointi primaarivaiheessa (n=29), metastasointi myöhemmin (n=37) ja ei metastasointia (n=51). Keskimääräinen alfainterferonihoidon kesto oli 11 kuukautta (kk) [0,5 – 32 kk]. Objektiivinen hoitovaste todettiin 17 %:lla, tautitilanne pysyi ennallaan 42 %:lla ja myöhäinen vaste (yli 12 kk:tta hoidon aloittamisesta) todettiin 3 %:lla. Aika vasteen saavuttamisesta taudin etenemiseen oli keskimäärin 8 kk ja elinaika 19,1 kk. Viiden vuoden elossaololuku oli 16 %. Jos metastasoituneella munuaissyöpäpotilaalla oli keuhkometastasointi, hän selvisi todennäköisemmin viisi vuotta kuin muut potilaat. Henkeä uhkaavia sivuvaikutuksia ei todettu. Yli 12 kk:n ajan kestävä alfainterferonihoito on hyödyllistä niille potilaille, jotka ovat saaneet objektiivisen hoitovasteen tai tautitilanne on pysynyt ennallaan. Positiivinen p53- ja Ki-67-ekspressio yhdessä viittaavat suureen metastasoinnin todennäköisyyteen. Positiivinen COX-2-ekspressio viittaa viivästyneeseen metastaasien ilmaantumiseen. Metastasoituneilla potilailla positiiviset p53- ja Ki-67-ekspressiot viittaavat huonoon ennusteeseen, mutta positiivinen COX-2 ekspressio viittaa suotuisaan ennusteeseen. Positiivinen COX-2- ja negatiivinen Ki-67-ekspressio yhdessä viittaavat parantuneeseen ennusteeseen metastasoituneessa munuaissyövässä.

Characterisation of Human Neutral and Lysosomal alpha-Mannosidases

Neutral alpha-mannosidase and lysosomal MAN2B1 alpha-mannosidase belong to glycoside hydrolase family 38, which contains essential enzymes required for the modification and catabolism of asparagine-linked glycans on proteins. MAN2B1 catalyses lysosomal glycan degradation, while neutral α-mannosidase is most likely involved in the catabolism of cytosolic free oligosaccharides. These mannose containing saccharides are generated during glycosylation or released from misfolded glycoproteins, which are detected by quality control in the endoplasmic reticulum. To characterise the biological function of human neutral α-mannosidase, I cloned the alpha-mannosidase cDNA and recombinantly expressed the enzyme. The purified enzyme trimmed the putative natural substrate Man9GlcNAc to Man5GlcNAc, whereas the reducing end GlcNAc2 limited trimming to Man8GlcNAc2. Neutral α-mannosidase showed highest enzyme activity at neutral pH and was activated by the cations Fe₂+, Co2+ and Mn₂+, Cu2+ in turn had a strong inhibitory effect on alpha-mannosidase activity. Analysis of its intracellular localisation revealed that neutral alpha-mannosidase is cytosolic and colocalises with proteasomes. Further work showed that the overexpression of neutral alpha-mannosidase affected the cytosolic free oligosaccharide content and led to enhanced endoplasmic reticulum associated degradation and underglycosylation of secreted proteins. The second part of the study focused on MAN2B1 and the inherited lysosomal storage disorder α-mannosidosis. In this disorder, deficient MAN2B1 activity is associated with mutations in the MAN2B1 gene. The thesis reports the molecular consequences of 35 alpha-mannosidosis associated mutations, including 29 novel missense mutations. According to experimental analyses, the mutations fall into four groups: Mutations, which prevent transport to lysosomes are accompanied with a lack of proteolytic processing of the enzyme (groups 1 and 3). Although the rest of the mutations (groups 2 and 4) allow transport to lysosomes, the mutated proteins are less efficiently processed to their mature form than is wild type MAN2B1. Analysis of the effect of the mutations on the model structure of human lysosomal alpha-mannosidase provides insights on their structural consequences. Mutations, which affect amino acids important for folding (prolines, glycines, cysteines) or domain interface interactions (arginines), arrest the enzyme in the endoplasmic reticulum. Surface mutations and changes, which do not drastically alter residue volume, are tolerated better. Descriptions of the mutations and clinical data are compiled in an α-mannosidosis database, which will be available for the scientific community. This thesis provides a detailed insight into two ubiquitous human alpha-mannosidases. It demonstrates that neutral alpha-mannosidase is involved in the degradation of cytosolic oligosaccharides and suggests that the regulation of this α-mannosidase is important for maintaining the cellular homeostasis of N-glycosylation and glycan degradation. The study on alpha-mannosidosis associated mutations identifies multiple mechanisms for how these mutations are detrimental for MAN2B1 activity. The α-mannosidosis database will benefit both clinicians and scientific research on lysosomal alpha‑mannosidosis.

Test Campaign Parameter Editor for a Graphical TTCN-3 Development Environment

This paper presents the design for a graphical parameter editor for Testing and Test Control Notation 3 (TTCN-3) test suites. This work was done in the context of OpenTTCN IDE, a TTCN-3 development environment built on top of the Eclipse platform. The design presented relies on an additional parameter editing tab added to the launch configurations for test campaigns. This parameter editing tab shows the list of editable parameters and allows opening editing components for the different parameters. Each TTCN-3 primitive type will have a specific editing component providing tools to ease modification of values of that type.

Differential Evolution approach and parameter estimation of chaotic

Parameter estimation still remains a challenge in many important applications. There is a need to develop methods that utilize achievements in modern computational systems with growing capabilities. Owing to this fact different kinds of Evolutionary Algorithms are becoming an especially perspective field of research. The main aim of this thesis is to explore theoretical aspects of a specific type of Evolutionary Algorithms class, the Differential Evolution (DE) method, and implement this algorithm as codes capable to solve a large range of problems. Matlab, a numerical computing environment provided by MathWorks inc., has been utilized for this purpose. Our implementation empirically demonstrates the benefits of a stochastic optimizers with respect to deterministic optimizers in case of stochastic and chaotic problems. Furthermore, the advanced features of Differential Evolution are discussed as well as taken into account in the Matlab realization. Test "toycase" examples are presented in order to show advantages and disadvantages caused by additional aspects involved in extensions of the basic algorithm. Another aim of this paper is to apply the DE approach to the parameter estimation problem of the system exhibiting chaotic behavior, where the well-known Lorenz system with specific set of parameter values is taken as an example. Finally, the DE approach for estimation of chaotic dynamics is compared to the Ensemble prediction and parameter estimation system (EPPES) approach which was recently proposed as a possible solution for similar problems.

Novel Methods for Error Modeling and Parameter Identification of a Redundant Serial-Parallel Hybrid Robot

To obtain the desirable accuracy of a robot, there are two techniques available. The first option would be to make the robot match the nominal mathematic model. In other words, the manufacturing and assembling tolerances of every part would be extremely tight so that all of the various parameters would match the “design” or “nominal” values as closely as possible. This method can satisfy most of the accuracy requirements， but the cost would increase dramatically as the accuracy requirement increases. Alternatively, a more cost-effective solution is to build a manipulator with relaxed manufacturing and assembling tolerances. By modifying the mathematical model in the controller, the actual errors of the robot can be compensated. This is the essence of robot calibration. Simply put, robot calibration is the process of defining an appropriate error model and then identifying the various parameter errors that make the error model match the robot as closely as possible. This work focuses on kinematic calibration of a 10 degree-of-freedom (DOF) redundant serial-parallel hybrid robot. The robot consists of a 4-DOF serial mechanism and a 6-DOF hexapod parallel manipulator. The redundant 4-DOF serial structure is used to enlarge workspace and the 6-DOF hexapod manipulator is used to provide high load capabilities and stiffness for the whole structure. The main objective of the study is to develop a suitable calibration method to improve the accuracy of the redundant serial-parallel hybrid robot. To this end, a Denavit–Hartenberg (DH) hybrid error model and a Product-of-Exponential (POE) error model are developed for error modeling of the proposed robot. Furthermore, two kinds of global optimization methods, i.e. the differential-evolution (DE) algorithm and the Markov Chain Monte Carlo (MCMC) algorithm, are employed to identify the parameter errors of the derived error model. A measurement method based on a 3-2-1 wire-based pose estimation system is proposed and implemented in a Solidworks environment to simulate the real experimental validations. Numerical simulations and Solidworks prototype-model validations are carried out on the hybrid robot to verify the effectiveness, accuracy and robustness of the calibration algorithms.

On state and parameter estimation in chaotic systems

State-of-the-art predictions of atmospheric states rely on large-scale numerical models of chaotic systems. This dissertation studies numerical methods for state and parameter estimation in such systems. The motivation comes from weather and climate models and a methodological perspective is adopted. The dissertation comprises three sections: state estimation, parameter estimation and chemical data assimilation with real atmospheric satellite data. In the state estimation part of this dissertation, a new filtering technique based on a combination of ensemble and variational Kalman filtering approaches, is presented, experimented and discussed. This new filter is developed for large-scale Kalman filtering applications. In the parameter estimation part, three different techniques for parameter estimation in chaotic systems are considered. The methods are studied using the parameterized Lorenz 95 system, which is a benchmark model for data assimilation. In addition, a dilemma related to the uniqueness of weather and climate model closure parameters is discussed. In the data-oriented part of this dissertation, data from the Global Ozone Monitoring by Occultation of Stars (GOMOS) satellite instrument are considered and an alternative algorithm to retrieve atmospheric parameters from the measurements is presented. The validation study presents first global comparisons between two unique satellite-borne datasets of vertical profiles of nitrogen trioxide (NO3), retrieved using GOMOS and Stratospheric Aerosol and Gas Experiment III (SAGE III) satellite instruments. The GOMOS NO3 observations are also considered in a chemical state estimation study in order to retrieve stratospheric temperature profiles. The main result of this dissertation is the consideration of likelihood calculations via Kalman filtering outputs. The concept has previously been used together with stochastic differential equations and in time series analysis. In this work, the concept is applied to chaotic dynamical systems and used together with Markov chain Monte Carlo (MCMC) methods for statistical analysis. In particular, this methodology is advocated for use in numerical weather prediction (NWP) and climate model applications. In addition, the concept is shown to be useful in estimating the filter-specific parameters related, e.g., to model error covariance matrix parameters.

Modeling and Parameter Estimation of Double-Star Permanent Magnet Synchronous Machines

The power rating of wind turbines is constantly increasing; however, keeping the voltage rating at the low-voltage level results in high kilo-ampere currents. An alternative for increasing the power levels without raising the voltage level is provided by multiphase machines. Multiphase machines are used for instance in ship propulsion systems, aerospace applications, electric vehicles, and in other high-power applications including wind energy conversion systems. A machine model in an appropriate reference frame is required in order to design an efficient control for the electric drive. Modeling of multiphase machines poses a challenge because of the mutual couplings between the phases. Mutual couplings degrade the drive performance unless they are properly considered. In certain multiphase machines there is also a problem of high current harmonics, which are easily generated because of the small current path impedance of the harmonic components. However, multiphase machines provide special characteristics compared with the three-phase counterparts: Multiphase machines have a better fault tolerance, and are thus more robust. In addition, the controlled power can be divided among more inverter legs by increasing the number of phases. Moreover, the torque pulsation can be decreased and the harmonic frequency of the torque ripple increased by an appropriate multiphase configuration. By increasing the number of phases it is also possible to obtain more torque per RMS ampere for the same volume, and thus, increase the power density. In this doctoral thesis, a decoupled d–q model of double-star permanent-magnet (PM) synchronous machines is derived based on the inductance matrix diagonalization. The double-star machine is a special type of multiphase machines. Its armature consists of two three-phase winding sets, which are commonly displaced by 30 electrical degrees. In this study, the displacement angle between the sets is considered a parameter. The diagonalization of the inductance matrix results in a simplified model structure, in which the mutual couplings between the reference frames are eliminated. Moreover, the current harmonics are mapped into a reference frame, in which they can be easily controlled. The work also presents methods to determine the machine inductances by a finite-element analysis and by voltage-source inverters on-site. The derived model is validated by experimental results obtained with an example double-star interior PM (IPM) synchronous machine having the sets displaced by 30 electrical degrees. The derived transformation, and consequently, the decoupled d–q machine model, are shown to model the behavior of an actual machine with an acceptable accuracy. Thus, the proposed model is suitable to be used for the model-based control design of electric drives consisting of double-star IPM synchronous machines.